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bioRxiv · 10.1101/2020.11.02.365189

Constructing Graphs from Genetic Encodings

Barabasi, D. L.·Czegel, D.
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Abstract

Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erd[o]s-Renyi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a nodes preferred connectivity.

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Barabasi, D. L., Czegel, D.. 2020-11-03. Constructing Graphs from Genetic Encodings. https://doi.org/10.1101/2020.11.02.365189

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